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All Journal Journal Automation Computer Information System (JACIS) Jurnal Ilmiah Informatika dan Komputer
Permadi, Dika Firman
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Aplikasi Sistem Pakar Diagnosis Penyakit Ayam Berbasis Web dengan Teknik Forward Chaining dan Certainty Factor Permadi, Dika Firman; Darmawan, Dwichi; Widyassari, Adhika Pramita
Journal Automation Computer Information System Vol. 5 No. 1 (2025): Mei
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v5i1.104

Abstract

Penelitian ini mengembangkan aplikasi sistem pakar berbasis web untuk mendiagnosis penyakit ayam menggunakan metode forward chaining yang dilengkapi Certainty Factor (CF). Sistem membantu peternak mengenali penyakit berdasarkan gejala yang sulit dibedakan, dengan aturan IF-THEN menghubungkan gejala ke penyakit spesifik. Pengujian menunjukkan akurasi 100% dalam mencocokkan diagnosis sistem dengan diagnosis manual ahli, membuktikan keandalan basis pengetahuan. Certainty Factor memberikan tingkat keyakinan diagnosis yang bervariasi berdasarkan nilai CF, seperti "Cukup Yakin" (CF 0.85 untuk Newcastle Disease) hingga "Sangat Yakin" (CF 0.90 untuk Flu Burung). Sistem ini mendukung keputusan cepat dan akurat, serta diharapkan terus diperbarui untuk meningkatkan relevansi dan efektivitasnya.
Penerapan Metode Profil Matching Dalam Pemilihan Tanah Lahan Pertanian Masuri, Masuri; Permadi, Dika Firman; Widyassari, Adhika Pramita
JIIFKOM (Jurnal Ilmiah Informatika dan Komputer) Vol 4 No 2 (2025): JIIFKOM
Publisher : Jurusan Informatika STTR Cepu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51901/jiifkom.v4i2.532

Abstract

Abstract The selection of agricultural land that matches crop characteristics is a crucial factor in improving productivity and sustainability in Indonesia's agricultural sector. This study aims to develop and implement the Profile Matching method in a decision support system to provide optimal recommendations for agricultural land selection. The Profile Matching method is used because it systematically and objectively compares the ideal crop requirements with the actual land conditions. This research involves identifying land criteria and sub-criteria such as soil texture, pH, nutrient content, rainfall, temperature, and topography, which are then analyzed using gap calculation stages, weighting, and grouping into core and secondary factors. The results show that the Profile Matching method is effective in ranking land suitability, thereby assisting farmers and policymakers in determining the best land for specific crops. The developed system provides a data-driven solution that minimizes subjectivity in decision-making and supports efforts to improve efficiency and productivity in sustainable agriculture.
Co-Authors Adhika Pramita Widyassari Darmawan, Dwichi Masuri, Masuri